Informatics strategies for phenotype characterization using lipid pathways

Lipids are key regulators in several complex pathobiological functional states. Phenotype of such complex state is characterised by integrated study at genomic and lipidomic levels in response to genetic or environmental perturbation. Current lipidomic studies, however, needs strategies for the elucidation of complex phenomena from the integration of large amounts of available data. Here we present informatics approaches to study lipid and gene expression profiles in the context of known lipid pathways. We compute the scaffold of theoretically possible lipids based on known lipid building blocks such as polar head groups and fatty acids and utilize recently developed nomenclature of lipids (1). Each compound entry is linked to the available information on lipid pathways and contains the information that can be utilized for its automated identification from UPLC/MS-based lipidomics experiments. Our global profiling involves screening of major lipids, including acylglycerols, phospholipids, sphingolipids, and cholesterol esters. Several data processing steps such as peak detection, alignment, and normalization are performed using MZmine software (2). The resulting peaks are identified utilizing a comprehensive spectral library of lipids, which afford automatic identification and profile comparison of several hundreds of lipid molecular species. Such data then leads to several analyses, such as multivariate exploratory analyses and correlation network analyses, as well as combined analyses of lipid and gene expression profiles in the context of pathways. We have identified the parallel associations between the elevated triacylglycerol levels and the ceramides, as well as the putative activated ceramide-synthesis pathways (3) in ob/ob mouse liver profiling. References 1. Fahy E, Subramaniam S, Brown HA, et al. A comprehensive classification system for lipids. J Lipid Res 2005;46(5):839-62. 2. Katajamaa M, Oresic M. Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics 2005;6:179. 3. Yetukuri L, Katajamaa M, Medina-Gomez G, Seppanen-Laakso T, Vidal-Puig A, Oresic M. Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis. BMC Systems Biology 2007;1(12):1752-0509.